Yeast Genome Project
Ilay Jacques Paz
2025-12-09
Abstract
Yeast species show strong variation in fermentation capacity and stress resistance for survival which can be applied to industrial settings. This study investigated genomic differences between Saccharomyces bayanus and Saccharomyces paradoxus to identify genes associated with fermentation and resistance. Using Oxford Nanopore long-read data, genome assemblies were generated and annotated to characterize functional gene content. Comparative analysis revealed that S. bayanus possessed a higher proportion of fermentation-related genes (8.5%) compared to S. paradoxus (7.9%), while resistance-associated genes were similarly enriched in S. bayanus (6.2%) versus S. paradoxus (5.7%). Unsurprisingly, S. bayanus, the domesticated yeast showed greater fermentation gene content than the supposedly more stress-resistant wild strain, S. paradoxus. However, S. bayanus was proven to have a greater proportion of resistance genes than the wild S. paradoxus. These findings challenge assumptions about the trade-off between fermentation efficiency and stress tolerance, suggesting that domestic lineages may maintain diverse stress resistance capabilities. The genomic distinctions identified here provide insight into evolutionary adaptations that shaped yeast ecology and may inform strain improvement strategies for biotechnological applications.
Background
Genomics is the comprehensive study of an organism’s complete genetic material, the genes within it and their functions.1 Genomics relies heavily on next-generation sequencing (NGS) technologies such as Oxford Nanopore, PacBio, Illumina which have revolutionized biological research by enabling rapid, high-throughput DNA sequencing at unprecedented scales.2 NGS is utilized in diverse omics approaches, including genomics (whole-genome sequencing and targeted sequencing), transcriptomics (RNA-seq), epigenomics (epigenetic modifications) and metagenomics (microbial community analysis)2. Oxford Nanopore Technology (ONT) represents a distinct approach within NGS, generating long reads that improve genome assembly continuity, particularly in repetitive or structurally complex regions. Unlike platforms such as Illumina, which produce short reads, ONT’s long reads make genome assembly much easier due to long reads behaving like large puzzle pieces.2 ONT distinguishes itself from another NGS long read based platform PacBio by having a significantly higher data output per run.2 This project focuses on comparing the genomes of Saccharomyces bayanus and Saccharomyces paradoxus to identify genes associated with fermentation efficiency and environmental stress responses. S. bayanus, widely used in winemaking and beermaking, while S. paradoxus is a wild yeast adapted to diverse natural environments with limited industrial use.3,4 By assembling and annotating both genomes, this project aims to identify key genetic variations that reflect evolutionary divergence and functional specialization. It is expected that S. bayanus will have a higher proportion of fermentation-related genes, whereas S. paradoxus will show a greater proportion on stress resistance due to its wild nature, providing insights into adaptations relevant for industrial applications like local craft breweries, wineries and biorefineries.
Methods
All analyses were performed across two computing environments using R versions 4.4.2 and 4.4.0 on macOS systems (Sonoma 14.5 and macOS 15.7.2 respectively, aarch64-apple-darwin20 platform). Command-line bioinformatics tools were executed in a Unix-based terminal environment. Matrix operations utilized either the Accelerate framework BLAS or libRblas with LAPACK version 3.12.0. The locale was set to en_US.UTF-8 with time zones America/Los_Angeles and America/Vancouver. Oxford Nanopore Technologies long-read sequencing data were obtained S. bayanus (dataset LV3C-1_JST) and S. paradoxus (dataset CP8-A_JSN). Reference genomes were obtained from NCBI GenBank: S. bayanus strain FM677 (accession GCA_019431265.1, assembly ASM1943126v1, genome size 12.2 Mb, haploid assembly type, submitted by Saint Louis University on August 1, 2021) and S. paradoxus strain CBS432 (NCBI RefSeq accession GCF_002079055.1, GenBank accession GCA_002079055.1, genome size 12 Mb, haploid assembly type designated as neotype, submitted by SC on March 21, 2017).5,6 Initial quality assessment of raw Nanopore reads was performed using Sequali (version 1.0.2) to generate comprehensive quality control reports including read length distributions, quality score profiles, and base composition statistics.7 Based on instructor guidance and examination of read length histograms from Sequali reports, reads were filtered using Filtlong version 0.3.1 with species-specific minimum length thresholds: 120 bp for S. bayanus and 140 bp for S. paradoxus.8 These cutoffs were selected to retain sufficient high-quality data for assembly while removing short, low-quality reads that could compromise assembly contiguity. Genome assembly was performed using Flye version 2.9.6, an assembler specifically designed for long, error-prone reads that employs repeat graph architecture to resolve them.9 Flye was executed with “–nano-hq” flag for high-quality Nanopore reads (>95% accuracy), with a genome size of 12 Mb (–genome-size 12m), and 8 threads (–threads 8) for both species. These parameters were selected to optimize assembly for Nanopore base calling with high per-read accuracy while leveraging parallel processing for computational efficiency. Assembly completeness and structural accuracy were evaluated using two tools pulled from Docker QUAST and BUSCO.10] QUAST version 5.2.0 was used to check and compare genome assemblies, providing rudimentary metrics for contiguity such as N50 and the number of contigs, along with more interesting metrics that evaluate the accuracy of the assembly such as misassemblies.11 Each assembled genome was aligned to its respective reference genome (S. bayanus assembly compared to GCA_019431265.1, S. paradoxus assembly compared to GCF_002079055.1) using default parameters. Metrics calculated included N50, total aligned length, genome fraction covered, number of contigs, largest contig size, NG50 (normalized to reference genome size), misassembly counts (including relocations, translocations, and inversions), and indel statistics. BUSCO (Benchmarking Universal Single-Copy Orthologs) version 6.0.0 was used to evaluate the completeness of the assembly by identifying the presence or absence of highly conserved, single-copy genes (orthologs) compared to a reference set for the saccharomycetaceae lineage.12 BUSCO was run in genome mode (-m genome) using the saccharomycetaceae_odb12 lineage dataset with 8 CPU threads (-c 8). A high BUSCO score indicates a more complete representation of the expected gene content for a given organism. BUSCO reports the percentage of complete (single-copy and duplicated), fragmented, and missing genes, providing an independent measure of assembly quality focused on biologically relevant gene content rather than just structural contiguity. Gene prediction was performed using Funannotate version 1.8.1, a comprehensive genome annotation pipeline designed for fungal genomes.13 Funannotate’ workflow included multiple steps: genome cleaning and sorting, repeat masking, gene prediction using multiple ab initio prediction algorithms integrated within the pipeline, and generation of standardized output files. Funannotate parameters were set to default values optimized for fungal genomes, with species name specified for each analysis (Saccharomyces bayanus and Saccharomyces paradoxus respectively). The pipeline produced protein-coding gene predictions in multiple formats including protein FASTA files for downstream functional annotation. Functional annotation of predicted proteins was conducted using the eggNOG database version 4.5.1 via eggNOG-mapper version 2.14 Protein sequences predicted by Funannotate were submitted to eggNOG-mapper using default parameters including automatic taxonomic scope assignment, diamond as the search method, and an e-value threshold of 0.001. The eggNOG database provides orthology assignments based on evolutionary relationships, enabling the transfer of functional annotations including Gene Ontology (GO) terms and Clusters of Orthologous Groups (COG) functional categories. Annotation results were exported in Excel format (.xlsx) containing comprehensive functional assignments for each predicted gene, including COG categories, GO terms (biological process, molecular function, cellular component), KEGG pathways, and protein domain predictions.
R Session Info For Mac Computer Lab T450 R version 4.4.0 (2024-04-24)
## Platform: aarch64-apple-darwin20 ## Running under: macOS 15.7.2 ## ##
Matrix products: default ## BLAS:
/Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
## LAPACK:
/Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;
LAPACK version 3.12.0 ## ## locale: ## [1]
en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 ## ## time
zone: America/Vancouver ## tzcode source: internal ## ## attached base
packages: ## [1] stats graphics grDevices utils datasets methods
base
## ## loaded via a namespace (and not attached): ## [1] compiler_4.4.0
fastmap_1.2.0 cli_3.6.5 tools_4.4.0
## [5] htmltools_0.5.8.1 rstudioapi_0.17.1 yaml_2.3.10
rmarkdown_2.30
## [9] knitr_1.50 xfun_0.53 digest_0.6.37 rlang_1.1.6
## [13] evaluate_1.0.5 ## Results
Initial quality assessment of raw Oxford Nanopore reads was performed using Sequali to evaluate read length distributions and identify optimal filtering parameters. The Sequali tool generated comprehensive read length histograms for S. bayanus and S. paradoxus, revealing substantial proportions of short reads that could compromise assembly contiguity. The unfiltered S. bayanus dataset exhibited a read length distribution with notable abundance of sequences below 120 bp, while the S. paradoxus dataset showed similar set of short reads below 140 bp. These quality control metrics informed the selection of species-specific minimum length thresholds for downstream filtering.
conda create -n sequali_env python=3.10 -c conda-forge
conda activate sequali_env
conda install sequali=2.9.6 -c conda-forge -c bioconda
sequali --outdir ~/sequali_reports 'Saccharomyces bayanus.fastq'
sequali --outdir ~/sequali_reports 'saccharomycesParadoxus.fastq'
filtlong --min_length 120 'Saccharomyces bayanus.fastq' > bayanus_filtered.fastq
sequali --outdir ~/sequali_reports bayanus_filtered.fastq
filtlong --min_length 140 'saccharomycesParadoxus.fastq' > paradoxus_filtered.fastq
sequali --outdir ~/sequali_reports paradoxus_filtered.fastq
Following Filtlong filtering with species-specific minimum length
thresholds (120 bp for S. bayanus and 140 bp for S. paradoxus), short
and poor-quality reads were effectively removed from both datasets. The
filtered read length distributions exhibited markedly improved profiles,
with the majority of reads concentrated in the mid-to-long range
suitable for de novo assembly. Quality metrics demonstrated the
effectiveness of filtering: the N50 read length for S. bayanus increased
from 1,783 bp to 1,809 bp, while S. paradoxus showed improvement from
2,487 bp to 2,542 bp. These enhanced read quality profiles provided
optimal input for subsequent genome assembly.
Assembly
Genome assembly with generated contiguous representations for both species. The S. bayanus assembly yielded a total length of 10,163,590 bp with an N50 of 11,571 bp and 1293 fragments, representing approximately 84% of the expected 12 Mb genome size. In contrast, the S. paradoxus assembly produced a total length of 8,868,681 bp with a superior N50 of 17,034 bp and 729 contigs, covering approximately 74% of the expected genome size. Despite the smaller total assembly size, S. paradoxus exhibited higher assembly contiguity as evidenced by the high N50 value. The largest fragment for S. bayanus measured 43665 bp, while S. paradoxus achieved 71455 bp, further supporting superior contiguity in the latter assembly. Mean coverage for S. bayanus was 9, compared to 7 for S. paradoxus, reflecting differences in read depth distribution.
flye --nano-hq data/bayanus_filtered.fastq --out-dir flye_output/ --threads 8 --genome-size 12m
#Total length: 10163590
#Fragments: 1293
#Fragments N50: 11571
#Largest frg: 43665
#Scaffolds: 0
#Mean coverage: 9
flye --nano-hq data/paradoxus_filtered.fastq--out-dir flye_outputP/ --threads 8 --genome-size 12m
#Total length: 8868681
#Fragments: 729
#Fragments N50: 17034
#Largest frg: 71455
#Scaffolds: 0
#Mean coverage: 7
BUSCO
BUSCO analysis using the saccharomycetaceae_odb12 lineage dataset provided independent evaluation of assembly completeness through identification of conserved single-copy orthologs. The S. bayanus assembly demonstrated 77.9% genome completeness and the S. paradoxus assembly achieved 70.1% genome completeness. These completeness scores indicate that both assemblies successfully captured the majority of expected core gene content for Saccharomycetaceae, though some fragmentation and gene loss relative to the reference set was evident. The moderate completeness scores suggest either incomplete assembly of certain genomic regions or potential biological divergence from the reference ortholog set.
export PATH="$PATH:/Applications/Docker.app/Contents/Resources/bin/"
type docker
docker login
docker run --rm \-v "":/data \-w /data \ezlabgva/busco:v6.0.0_cv1 \busco -i assembly.fasta -o busco_output -l saccharomycetaceae_odb12 -m genome -c 8
#bayanus
docker run --rm \-v "":/data \-w /data \ezlabgva/busco:v6.0.0_cv1 \busco -i assemblyP.fasta -o busco_outputP -l saccharomycetaceae_odb12 -m genome -c 8
#paradoxus
#NOTE FILL IN WHERE YOUR DATA IS WHERE THERE ARE ""QUAST
QUAST analysis comparing assembled genomes to their respective reference sequences revealed substantial differences in alignment metrics between species. The S. bayanus assembly aligned to only 25.9% of its reference genome (GCA_019431265.1), with 6 misassemblies detected comprising 2 relocations, 4 translocations, and 0 inversions.and 1,248.07 mismatches per 100 kbp. In stark contrast, the S. paradoxus assembly covered 71.7% of its reference genome (GCF_002079055.1), though with a higher count of 34 misassemblies and substantially fewer mismatches at 234.13 per 100 kbp. In S. paradoxus misassemblies comprising 23 relocations, 11 translocations, and 0 inversions. The NG50 value for S. paradoxus was 11,874 while S. bayanus did not have one. The dramatically lower genome fraction coverage in S. bayanus suggests substantial genomic divergence from the reference strain or potential assembly challenges in capturing strain-specific sequences.
docker pull quay.io/biocontainers/quast:5.3.0--py313pl5321h5ca1c30_2
#bayanus
-v "":/data \
-w /data \
quay.io/biocontainers/quast:5.3.0--py313pl5321h5ca1c30_2 \
quast.py assembly.fasta \
-r /data/bayanusreferenceassembly.fna \-o quast_output
#paradoxus
docker run --rm \
-v "":/data \
-w /data \
quay.io/biocontainers/quast:5.3.0--py313pl5321h5ca1c30_2 \
quast.py assemblyP.fasta \
-r /data/paradoxusreferenceassembly.fna \
-o quastP_output
#NOTE FILL IN WHERE YOUR DATA IS WHERE THERE ARE ""
### Annotation
Gene prediction performed using Funannotate v1.8.1 generated
comprehensive protein-coding gene sets for both species, which were
subsequently functionally annotated using eggNOG-mapper v2 and the
eggNOG database v4.5. The annotation pipeline assigned Clusters of
Orthologous Groups (COG) categories and Gene Ontology (GO) terms to
predicted proteins, enabling comparative functional genomics analysis.
Comparative analysis of COG category distributions revealed that S.
bayanus’ genome possessed 7.9% fermentation-related genes and 5.7% as
resistance-related genes based on COG functional annotations. S.
paradoxus genome contained 8.5% fermentation-related genes and 6.2%
resistance-related genes. Keyword-based searches within GO terms and COG
descriptions for fermentation-associated processes were (“glycolysis,”
“ethanol,” “alcohol,” “anaerobic”) and resistance-related keyword
searches were (“stress,” “resistance,” “defense”). These functional
annotations provide the foundation for comparative analysis of metabolic
capabilities between the two species.
#bayanus
docker run --rm \-v "":/data \-w /data \--platform linux/amd64 \nextgenusfs/funannotate \funannotate mask -i assembly.fasta -o tr_masked_assembly.fasta
#paradoxus
docker run --rm \-v "":/data \-w /data \--platform linux/amd64 \nextgenusfs/funannotate\funannotate predict -i tr_masked_assembly.fasta -o /data/output -s "Saccharomyces bayanus"
#paradoxus
docker run --rm \-v "/Users/ipaz00/Downloads/BIOL4315_R/project/BIOL-4315-project":/data \-w /data \--platform linux/amd64 \nextgenusfs/funannotate\funannotate mask -i assemblyP.fasta -o tr_masked_assemblyP.fasta
docker run --rm \-v "/Users/ipaz00/Downloads/BIOL4315_R/project/BIOL-4315-project":/data \-w /data \--platform linux/amd64 \nextgenusfs/funannotate\funannotate predict -i tr_masked_assemblyP.fasta -o /data/outputP -s "Saccharomyces paradoxus"
#NOTE FILL IN WHERE YOUR DATA IS WHERE THERE ARE ""
##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
Figure 8. Relative abundance of COG categories in S. paradoxus and S. bayanus genomes. Bubble size is proportional to the percentage of genes assigned to each functional group (Fermentation = C+G; Resistance = V+M+W+P+Q; Other = all remaining categories).
## Loading required package: AnnotationDbi## Loading required package: stats4## Loading required package: BiocGenerics##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:dplyr':
##
## combine, intersect, setdiff, union## The following objects are masked from 'package:stats':
##
## IQR, mad, sd, var, xtabs## The following objects are masked from 'package:base':
##
## anyDuplicated, aperm, append, as.data.frame, basename, cbind,
## colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
## get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,
## match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
## Position, rank, rbind, Reduce, rownames, sapply, saveRDS, setdiff,
## table, tapply, union, unique, unsplit, which.max, which.min## Loading required package: Biobase## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.## Loading required package: IRanges## Loading required package: S4Vectors##
## Attaching package: 'S4Vectors'## The following objects are masked from 'package:dplyr':
##
## first, rename## The following object is masked from 'package:utils':
##
## findMatches## The following objects are masked from 'package:base':
##
## expand.grid, I, unname##
## Attaching package: 'IRanges'## The following objects are masked from 'package:dplyr':
##
## collapse, desc, slice##
## Attaching package: 'AnnotationDbi'## The following object is masked from 'package:dplyr':
##
## select## ##
## Attaching package: 'tidyr'## The following object is masked from 'package:S4Vectors':
##
## expand## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
Figure 9. Fermentation-Related Gene Ontology (GO) Terms in S. bayanus and S. paradoxus
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
Figure 10.Resistance and Stress Related Gene Ontology (GO) Terms in S. bayanus and S. paradoxus
Discussion
The quality control and assembly workflow revealed interesting characteristics in the genomic datasets for S. bayanus and S. paradoxus that have important implications for analyzing downstream results. The unfiltered Sequali read length histograms demonstrated that both species had substantial short reads requiring removal to improve future assembly. This superior read length distribution in S. paradoxus likely contributed to the higher assembly contiguity observed in Flye assembly metrics, where S. paradoxus achieved an N50 of 17034 bp despite a smaller total assembly size. The complicated relationship between dataset size and assembly quality in this study where S. bayanus possessed more sequence data yet yielded lower-quality metrics—highlights the importance of read length distribution and coverage over raw data quantity. The N50 metric, representing the fragment length at which 50% of the total assembly is contained in contigs of that size or larger, serves as the best indicator of assembly contiguity. The BUSCO completeness scores of 77.9% for S. bayanus and 70.1% for S. paradoxus indicate that both assemblies successfully captured the majority of conserved core genes expected in Saccharomycetaceae genomes, though neither achieved a completeness score greater than 95 typically seen in reference genome in a perfect world. It is important to recognize that moderate BUSCO scores do not indicate a bad assembly; rather, they reflect the reality including of gene loss events or potential abscences from the OrthoDB reference set. The saccharomycetaceae_odb12 lineage dataset is based on a consensus of sequenced Saccharomycetaceae genomes and may not perfectly represent the gene content of all strains. The stark difference in QUAST genome fraction coverage between S. bayanus (25.9%) and S. paradoxus (71.7%) relative to their respective reference genomes provides critical insight into variation within these species. The dramatically low genome fraction of S. bayanus strongly suggests that the Langara College isolate (dataset LV3C-1_JST) represents a strain with substantial genomic divergence from the reference strain FM677 (GCA_019431265.1). This divergence could manifest as large-scale structural variations, presence of strain-specific genomic islands, differences in repeat content, or even taxonomic misclassification of one of the strains. The low misassembly count (6) combined with high mismatch rate (1,248.07 per 100 kbp) in S. bayanus suggests that the assembly itself is structurally sound, but the sequences diverge substantially at the nucleotide level from the reference. This pattern is more consistent with genuine biological divergence than with technical assembly artifacts. In contrast, the S. paradoxus assembly showed higher congruence with its reference (CBS432, GCF_002079055.1), covering 71.7% of the reference genome. However, the elevated misassembly count (34) compared to S. bayanus (6) warrants attention. The substantially lower mismatch rate (234.13 per 100 kbp) and indel rate (141.16 per 100 kbp) in S. paradoxus compared to S. bayanus further support that the S. paradoxus isolate is more closely related to its reference strain, facilitating more confident comparative genomic interpretations. The comparative functional annotation revealed unexpected patterns in fermentation and stress resistance gene content that challenge initial predictions. Contrary to the hypothesis that the domesticated S. bayanus would show enrichment in fermentation genes while the wild S. paradoxus would exhibit greater stress resistance capacity, both species showed similar proportions of genes in these functional categories, with S. bayanus actually demonstrating slightly higher representation in both fermentation (8.5% vs. 7.9%) and resistance (6.2% vs. 5.7%) gene categories.However, these quantitative comparisons must be interpreted with caution given the substantial differences in assembly quality and completeness between the two species. The dramatically different genome fraction coverage values (25.9% vs. 71.7%) indicate that we are comparing genomic datasets of fundamentally different completeness and potentially different biological content.Additionally, the reliance on keyword-based searches for functional categorization, while useful for broad comparisons, introduces limitations including potential misclassification of genes based on annotation text matching rather than true functional equivalence. A major limitation in the annotation-based comparative analysis is the constraint of visualizing large numbers of genes in figures. This limitation is particularly problematic for complex, multigenic phenotypes like fermentation and stress resistance, where dozens to hundreds of genes contribute to the overall physiological capacity. The keyword-based approach also risks missing functionally relevant genes that are annotated with synonymous or related terms not included in the search parameters, or conversely including false positives where keywords appear in annotations for genes with tangential functional relevance. Contrary to the classical expectation that domestication for fermentation relaxes selection of stress tolerance, the sequenced S. bayanus isolate appears to devote a higher proportion of its predicted gene content to both fermentation-related genes (8.5% vs. 7.9% ) and stress-resistance genes (6.2% vs. 5.7%). This pattern challenges the assumption of a simple trade-off between rapid fermentation and environmental resilience. The simultaneous enrichment of both functional categories in S. bayanus suggests that fermentation itself does not reduce stress-response systems even in industrial settings. Thus, high fermentation capacity and stress tolerance may be functionally coupled rather than antagonistic traits. However, these conclusions must remain provisional. Until comparable, high-quality assemblies are obtained for both strains, quantitative gene-content comparisons remain unreliable. If confirmed with improved assemblies, the apparent co-enrichment of fermentation and stress-tolerance genes in domesticated S. bayanus would highlight the untapped potential of existing industrial strains for further optimisation. Conversely, the extensive genomic divergence of the sequenced S. bayanus isolate from its reference suggesting substantial strain-level variation underscores the value of reference genomes.S. paradoxus, may harbour alternative stress-resistance mechanisms or allelic variants not captured by COG and GO categories. Local craft breweries, wineries, and biorefineries in British Columbia could benefit from systematic characterisation of regionally isolated strains. Such isolates may exhibit fermentation profiles and stress tolerances finely tuned to local substrates and conditions, potentially conferring distinctive flavour compounds or enhanced process robustness. The most severe limitation is the marked disparity in assembly completeness (25.9% vs. 71.7% genome fraction coverage), which precludes reliable quantitative comparison of gene content between the two species. Future work should prioritise: Ensuring the datasets are better, scaffolding to produce chromosome-level references, multiple strains of S. bayanus and S.paradoxus being sequenced, taxonomic validation that the sequenced S. bayanus is actually S. bayanus. Only with higher-quality,complete genomes can reasonable evolutionary and biotechnological conclusions be drawn from comparative gene-content analysis in S. bayanus and S. paradoxus.
sessionInfo()## R version 4.4.2 (2024-10-31)
## Platform: aarch64-apple-darwin20
## Running under: macOS Sonoma 14.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/Los_Angeles
## tzcode source: internal
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] stringr_1.5.2 tidyr_1.3.1 GO.db_3.20.0
## [4] AnnotationDbi_1.68.0 IRanges_2.40.1 S4Vectors_0.44.0
## [7] Biobase_2.66.0 BiocGenerics_0.52.0 scales_1.4.0
## [10] RColorBrewer_1.1-3 dplyr_1.1.4 ggplot2_4.0.0
## [13] readxl_1.4.5
##
## loaded via a namespace (and not attached):
## [1] sass_0.4.10 generics_0.1.4 stringi_1.8.7
## [4] RSQLite_2.4.3 digest_0.6.37 magrittr_2.0.4
## [7] evaluate_1.0.5 grid_4.4.2 fastmap_1.2.0
## [10] blob_1.2.4 cellranger_1.1.0 jsonlite_2.0.0
## [13] GenomeInfoDb_1.42.3 DBI_1.2.3 httr_1.4.7
## [16] purrr_1.1.0 UCSC.utils_1.2.0 Biostrings_2.74.1
## [19] jquerylib_0.1.4 cli_3.6.5 rlang_1.1.6
## [22] crayon_1.5.3 XVector_0.46.0 bit64_4.6.0-1
## [25] withr_3.0.2 cachem_1.1.0 yaml_2.3.10
## [28] tools_4.4.2 memoise_2.0.1 GenomeInfoDbData_1.2.13
## [31] vctrs_0.6.5 R6_2.6.1 png_0.1-8
## [34] lifecycle_1.0.4 zlibbioc_1.52.0 KEGGREST_1.46.0
## [37] bit_4.6.0 pkgconfig_2.0.3 pillar_1.11.1
## [40] bslib_0.9.0 gtable_0.3.6 glue_1.8.0
## [43] xfun_0.53 tibble_3.3.0 tidyselect_1.2.1
## [46] rstudioapi_0.17.1 knitr_1.50 farver_2.1.2
## [49] htmltools_0.5.8.1 rmarkdown_2.30 labeling_0.4.3
## [52] compiler_4.4.2 S7_0.2.0